Qdrant - Mem0 — ContextQMD

Qdrant is an open-source vector search engine. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data.

Usage

<CodeGroup> ```python Python import os from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = { "vector_store": { "provider": "qdrant", "config": { "collection_name": "test", "host": "localhost", "port": 6333, } } }

m = Memory.from_config(config) messages = [ {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"}, {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."}, {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."}, {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."} ] m.add(messages, user_id="alice", metadata={"category": "movies"})


```typescript TypeScript
import { Memory } from 'mem0ai/oss';

const config = {
  vectorStore: {
    provider: 'qdrant',
    config: {
      collectionName: 'memories',
      embeddingModelDims: 1536,
      host: 'localhost',
      port: 6333,
    },
  },
};

const memory = new Memory(config);
const messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });

</CodeGroup>

Config

Let's see the available parameters for the qdrant config:

<Tabs> <Tab title="Python"> | Parameter | Description | Default Value | | --- | --- | --- | | `collection_name` | The name of the collection to store the vectors | `mem0` | | `embedding_model_dims` | Dimensions of the embedding model | `1536` | | `client` | Custom client for qdrant | `None` | | `host` | The host where the qdrant server is running | `None` | | `port` | The port where the qdrant server is running | `None` | | `path` | Path for the qdrant database | `/tmp/qdrant` | | `url` | Full URL for the qdrant server | `None` | | `api_key` | API key for the qdrant server | `None` | | `on_disk` | For enabling persistent storage | `False` | </Tab> <Tab title="TypeScript"> | Parameter | Description | Default Value | | --- | --- | --- | | `collectionName` | The name of the collection to store the vectors | `mem0` | | `embeddingModelDims` | Dimensions of the embedding model | `1536` | | `host` | The host where the Qdrant server is running | `None` | | `port` | The port where the Qdrant server is running | `None` | | `path` | Path for the Qdrant database | `/tmp/qdrant` | | `url` | Full URL for the Qdrant server | `None` | | `apiKey` | API key for the Qdrant server | `None` | | `onDisk` | For enabling persistent storage | `False` | </Tab> </Tabs>